{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Anomaly Detection\n",
    "* What are Outliers ?\n",
    "* Statistical Methods for Univariate Data\n",
    "* Using Gaussian Mixture Models\n",
    "* Fitting an elliptic envelope\n",
    "* Isolation Forest\n",
    "* Local Outlier Factor\n",
    "* Using clustering method like DBSCAN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Outliers\n",
    "* New data which doesn't belong to general trend (or distribution) of entire data are known as outliers.\n",
    "* Data belonging to general trend are known as inliners.\n",
    "* Learning models are impacted by presence of outliers.\n",
    "* Anomaly detection is another use of outlier detection in which we find out unusual behaviour.\n",
    "* Data which were detected outliers can be deleted from complete dataset.\n",
    "* Outliers can also be marked before using them in learning methods"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Statistical Methods for Univariate Data\n",
    "* Using Standard Deviation Method - zscore\n",
    "* Using Interquartile Range Method - IRQ"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Using Standard Deviation Method\n",
    "* If univariate data follows Gaussian Distribution, we can use standard deviation to figure out where our data lies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.random.normal(size=1000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Adding More Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[-5:] = [3.5,3.6,4,3.56,4.2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import zscore"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Detecting Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-3.87528698,  3.5       ,  3.6       ,  4.        ,  3.56      ,\n",
       "        4.2       ])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[np.abs(zscore(data)) > 3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Using Interquartile Range\n",
    "* For univariate data not following Gaussian Distribution IQR is a way to detect outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.stats import iqr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.random.normal(size=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "data[-5:]=[-2,9,11,-3,-21]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "iqr_value = iqr(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "lower_threshold = np.percentile(data,25) - iqr_value*1.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "upper_threshold = np.percentile(data,75) + iqr_value*1.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.947881869860901"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "upper_threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-2.834800141837741"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lower_threshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ -3.24344663,  -3.        , -21.        ])"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[np.where(data < lower_threshold)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3.09183163,  3.1700815 ,  9.        , 11.        ])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[np.where(data > upper_threshold)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Using Gaussian Mixture Models\n",
    "* Data might contain more than one peaks in the distribution of data.\n",
    "* Trying to fit such multi-model data with unimodel won't give a good fit.\n",
    "* GMM allows to fit such multi-model data.\n",
    "* Configuration involves number of components in data, n_components.\n",
    "* covariance_type controls the shape of cluster\n",
    "  - full : cluster will be modeled to eclipse in arbitrary dir\n",
    "  - sperical : cluster will be spherical like kmeans\n",
    "  - diag : cluster will be aligned to axis\n",
    "* We will see how GMM can be used to find outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Number of samples per component\n",
    "n_samples = 500\n",
    "\n",
    "# Generate random sample, two components\n",
    "np.random.seed(0)\n",
    "C = np.array([[0., -0.1], [1.7, .4]])\n",
    "C2 = np.array([[1., -0.1], [2.7, .2]])\n",
    "#X = np.r_[np.dot(np.random.randn(n_samples, 2), C)]\n",
    "          #.7 * np.random.randn(n_samples, 2) + np.array([-6, 3])]\n",
    "X = np.r_[np.dot(np.random.randn(n_samples, 2), C),np.dot(np.random.randn(n_samples, 2), C2)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "X[-5:] = [[4,-1],[4.1,-1.1],[3.9,-1],[4.0,-1.2],[4.0,-1.3]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x179ba650e48>"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x179b92185c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:,0], X[:,1],s=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.mixture import GaussianMixture"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [],
   "source": [
    "gmm = GaussianMixture(n_components=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GaussianMixture(covariance_type='full', init_params='kmeans', max_iter=100,\n",
       "        means_init=None, n_components=3, n_init=1, precisions_init=None,\n",
       "        random_state=None, reg_covar=1e-06, tol=0.001, verbose=0,\n",
       "        verbose_interval=10, warm_start=False, weights_init=None)"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gmm.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = gmm.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2], dtype=int64)"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred[:50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x179ba86e588>"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x179ba7adc88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:,0], X[:,1],s=10,c=pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. Fitting Elliptical Envelope\n",
    "* The assumption here is, regular data comes from known distribution ( Gaussion distribution )\n",
    "* Inliner location & variance will be calculated using `Mahalanobis distances` which is less impacted by outliers.\n",
    "* Calculate robust covariance fit of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "X,_ = make_blobs(n_features=2, centers=2, cluster_std=2.5, n_samples=1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x179baaba710>"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x179baa04a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:,0], X[:,1],s=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.covariance import EllipticEnvelope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "ev = EllipticEnvelope(contamination=.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "EllipticEnvelope(assume_centered=False, contamination=0.1, random_state=None,\n",
       "         store_precision=True, support_fraction=None)"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ev.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster = ev.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x179bad54710>"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x179bacf2d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X[:,0], X[:,1],s=10,c=cluster)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. Isolation Forest\n",
    "* Based on RandomForest\n",
    "* Useful in detecting outliers in high dimension datasets.\n",
    "* This algorithm randomly selects a feature & splits further.\n",
    "* Random partitioning produces shorter part for anomolies.\n",
    "* When a forest of random trees collectively produce shorter path lengths for particular samples, they are highly likely to be anomalies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "rng = np.random.RandomState(42)\n",
    "\n",
    "# Generate train data\n",
    "X = 0.3 * rng.randn(100, 2)\n",
    "X_train = np.r_[X + 2, X - 2]\n",
    "# Generate some regular novel observations\n",
    "X = 0.3 * rng.randn(20, 2)\n",
    "X_test = np.r_[X + 2, X - 2]\n",
    "# Generate some abnormal novel observations\n",
    "X_outliers = rng.uniform(low=-4, high=4, size=(20, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import IsolationForest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.r_[X_train,X_test,X_outliers]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [],
   "source": [
    "iso = IsolationForest(behaviour='new', contamination='auto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IsolationForest(behaviour='new', bootstrap=False, contamination='auto',\n",
       "        max_features=1.0, max_samples='auto', n_estimators=100,\n",
       "        n_jobs=None, random_state=None, verbose=0)"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iso.fit(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = iso.predict(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x179bc4f1a90>"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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oCjhlUHt+24rdz71dkYqvQcKTcVNnQvRe0LUtZdIrYMSD0DAT3FVI8Xd63fXy4oMv8+vTb8dxhK12m8hvnv8pgWBHk6XygyV2Y/rIhXd+j/Hbb87yz6qY8v3DKd9suN8h9YhT9A3UKUbX3QDul7T0nccgMQ/cFesvA5z6FCSMRPZB4691XLFG0XW/QMofwSk9Hy0+A62a3KpAHaSX4ZSe5xVPfugtzat1UHoZTuGUTmOu/nI1j/7+KQqLC5h64dEUlXqfLP54+b0kGr2bvosWLOa9lz9k5wO7GI0zgFliN6aPhMIhvnXpsX6H0StScDhaewnrj0pphNRCtOZ0dNifcCIHeitF1l4Mktm/dPjfgDJgXftaQVqm/6t2sCSwM6rl+NofQHqp96B2OhreCwmMXO+URDzJOXtOZ21VLYGgw/xZb3HL3J8DUDq8hKrPV6EKrutSOmzgjYXvCetjN8ZsWGgXWvrNm5YKaKV2uve18V68HY6ioEkk8SaM+BvrrQ/jjESG/AwATX0K1Yfh9afjlS25HCfQquvFbb2omNNyU7Wd6qWria6L4qZdkvEUC+d91HzsinvPZ/OtN6V4SBGnXv1NJu40cLrFNoa12I0xGyRD/4A23AXuWqT4DLT+Doj9q6VA065Hwe0g8Qbe0EYHglsgwe3Q4FbeDVEEgjuArkSrj0ULp2Z2ZqrDu8kaREouQErObBtA8Q+g/jbvk0BoFwhs0WGc5WNGUFRaSCKWxAk4bLtHy2zW8duP4e4POrpZm58ssRtjNkicEqT04ubHWnY9uMszSVyg9EqvXOlFqMYg+TYUHo9E9vNOGP4AGn3Q22819jjNY9pj/4bw/nhpKOl9ddbf+cgpmYYWHAJuHYR29mbKdiAcCfHdn53Ib38wAzftUjaiDFUdlHufWmI3xvSI4zjosL95W9Q5JYjj3RQWiSBDrl3/hMRrUH8z601SUiByuLdcQfJdiOwLhSd0eM3OFghr755rH8JNK6C8+dzbfPzGp4NyRVFL7MaYHhMRCI7rVlmtu54OZ54GJyCFRyJF2bvBHC4MN3+vrhJp9XgwsZunxpi+1eHG0sNh+MOIZDfxXnHP+QwpLyUQDHD8RVMYP2kjNu/OA9ZiN8b0KRl6A1ozDdIrgSII7wxDb8Fxsj9BaPu9tuafVTMHbd96E0vsxpg+JcGtkJHP9+81B3FSB+uKMcaYvGOJ3Rhj8owldmOMyTOW2I0xJs9kJbGLyEwRqRKRd7NRnzHGmI2XrRb7X4AjslSXGSQ+/+BL7rzkrzzy2ydJJTtY4c8Ys1GyMtxRVeeKyIRs1GUGh5qqWs7bezrRukbCBWEWLVjMpXef43dYxuQF62M3vvjsnc8RBBQSjQnmz3pr/TLvfs4Td81i0VuL+z9AYwawfpugJCLTgGkA48Z1b40Jk78m7jiWpo3UwwUhdjtkpzbH35/3EZcd1rKg1K+e+jE7HbB9v8aYLdXL1vDkjFkUDynimB98jXDB4Fy/xPSffkvsqjoDmAFQUVGhXRQ3eW7YJkP53SvX8cRdsxg5tpzjzj+yzfG5D71MPJpofvz8/S8NyMQeb4xzTsXl1FavIxAM8Mbsd/jlk1f6HZbJc7akgPHN+EljOee3Z3R4bMtdJhIpihCPxikoirDVbhP6N7gsWf5pFY31MdIpl3TK5a05NnDM9L2sJHYRuR/4ClAuIkuBn6jqn7NRtxmcDjvtQNasqGHeE/PZ44hdOfLMQ/0OaaNsMmEkoUiQWIMQDAfYds+t/Q7JDALS1M/ZnyoqKrSysrLfr2uMH778ZDn/vPkJSoYWc9IV36C4rMjvkMwAJSLzVbWiq3LWFWNMH9t8q0254A/f8zsMM4jYcEdjjMkzltiNMSbPWGI3xpg8Y4ndGGPyjCV2Y4zJM5bYjTEmz1hiN8aYPGOJ3Rhj8owldmOMyTOW2I0xJs9YYjfGmDxjid0YY/KMJXZjjMkzltiNMSbPWGI3xpg8Y4ndGGPyjCV2Y4zJM1lJ7CJyhIh8KCKfiMgV2ajTGGPMxul1YheRAHA7cCQwCfi2iEzqbb3GGGM2TjZa7HsCn6jqp6qaAB4Ajs1CvcYYYzZCNhL75sAXrR4vzTzXhohME5FKEalctWpVFi5rjDGmI9lI7NLBc7reE6ozVLVCVStGjhyZhcsaY4zpSDYS+1JgbKvHY4BlWajXGGPMRshGYn8d2FpEJopIGDgJeDwL9RpjjNkIvU7sqpoCzgWeARYCD6rqe72t1xg/pFNprjv5VqYUn8K5e13B2lW1fodkTI9lZRy7qv5HVbdR1S1V9bps1GmMH56//yVeebySeGOCTxYs5s/T7/M7JGN6zGaeGtNKQ20UdV0A0sk0dTX1PkdkTM9ZYjemlUNPOYDhmw6jsKSAorJCTr36m36HZEyPBf0OwJhcUjqshD+/fytffrycUePKKS4r8jskY3rMErsx7YQjISbuOM7vMIzZaNYVY4wxecYSuzHG5BlL7D758pPlPPSbx3nl35V+h2KMyTPWx+6Dqs9X8cPdLycRSxAMB/nutSfyzYu+7ndYxpg8YS12H7z14vu4qqSSaWINcZ67779+h2SMySOW2H2wxc7jmyfBRArD7LDvtj5HZIzJJ9YV44Mtd5nANQ9dwhN3PcvEncZz6tXH+x2SMSaPWGL3yZ5H7saeR+7mdxjGmDxkXTHGGJNnLLEbY0yescQ+SCXiSVTX28HQGJMHLLEPMqrKDf93G18vPoWp5afzYeUiv0MyxmSZJfZB5u257/PSw/NwXaW+poFbpt3pd0jGmCyzxD7IqKuISPNjN+36GI0xpi/0KrGLyAki8p6IuCJSka2gTN/Z+aBJTD58Z4KhAIUlBZz/h+/5HZIxJst6O479XWAqcFcWYjH9wHEcfvrwpdTV1FNYUkAwZFMZjMk3vfqrVtWFQJuP9mZgKB1W4ncIxpg+0m997CIyTUQqRaRy1apV/XVZY4wZdLpssYvIbGB0B4euUtXHunshVZ0BzACoqKiwAdTGGNNHukzsqnpYfwRijDEmO2y4Yx7R1FLcVUfgrtgJt/Yam1lqzCDV2+GOx4nIUmAf4EkReSY7YZmNoet+CunFQBxij0PiJZ8jMsb4oVeJXVUfVdUxqhpR1U1U9WvZCsxsBF0HtEw40tQS3Opv4K7cC7d+hn9xGWP6lXXF5BEpvRSkECiEwHhofAJSH4DWQP1taPI9v0M0xvQDm52SRyS8B4ycC+4qCExAq4+iuQUvAXDXNJd1o49A4yMQ2gUpvRCRkD9BG2OyzhJ7nhFnCDhDANCSC6H2ci+pB8ZBeC/v+firsO5aoBGSb6M4SNnFPkZtjMkmS+x5zCk8Cg3vCulVENoekbB3IPUhkM6UikHydbTxPxDeBQls7le4xpgssT72AUyT7+NW7Ye7YhLuuus7LCOBzZDwLi1JHSByIEgQKAAikHwXXXcVWn00mvygX2I3xvQdS+wDmNZe7vWnk4Lo/WjynW6dJ8EJyIjHkLIrILwfkABtAI2ijY/3aczGmL5nXTEDiKpC7FE08R5SeARorOWgSJvHml6J1t0I2oiUXICEtkHdtdD4OEgxFB4Dod0hvgCIAHGgAAlO7O+XZYzJMkvsA4hGZ0Ld74BGtPEhKL0M6n4NKIT2gNDklrJrTof0IkDR+By0fDbUnArpKiAAjQ9CciHehzaFwASIHA6Fx/vx0owxWWSJfSCJvQg0Zh4ogsKol72JSc6mbZdPziR1TwrWfAfc5TTfNE0uaHVcIL0E0p/gDY+0HjpjBjL7Cx5ICg4CCjMPBMK7I06Jd4O0/Zr4we3aPna/oGUkTAAYQsv7unr/4vMg9lTfxG6M6TfWYh9ApOgMVIZB0utjl9AkANy6O6DhTnCKYdgfcUI7wNDboHoKXjJP0tI6D0BwR0h91OE1NPkBmngNgjsgRd9CxN77jRloLLEPICKCFE3F243Q4yYXQsMtmQeNsPpkdNT/oOYsvGSewmuhpzOP0163S3OXToDmX4PASIjOzJQtQLUWKfl+P7wyY0w2WXOsD7365HwuOugabjzjdhpqG3pdn6aWog1/Q+NzUbced/XJsHpqu1KNaMODkF4OxPD6zFO0tNgBraXlvz4MZVdC+Sxw62gzcSn+Ysspbj0a/x+a+qLXr8P4IxaN89DN/+Zv1z7ImhU1fodj+pC12PvI0o+W8fMTbyYeTbDw1Y9pWNvATx+5rMf1qKYh/hyaroL6m0GTIA4EJ0HyTVqv5tisoePJSq1qBWdT0CgUHIIUnoC6cdC1bYulPkXdeiCBVn8dtBE0BcNuRyIHtNTm1nvLBQcmII7tpZqrfnLcr3n3vwtJp9I89afn+OsntxGO2BpB+cgSex/58uPlBIIBAFKJFIveWrJR9WjtJRCf4yVUEpkngeT8bpwteP/FTV0yrRSfj0T2QIJjvSrrru7g4g0Q+49Xj9Z7iR3Q+ruaE7umPkdXH4/X0g/AiIeR4LievkzTD95+4X1SSe/3oL42ysrFVYzd1paQyEfWFdNHJu27LeGCMJGiCAXFEY4669Ae1+FNSHrKa1k3JXWg2+/HzjBkkzeguINPCnVXotVH4zY+6T2O/Ys23TVeBSBFENik1aEQSBFu1VdwV1ag664Brcsk/jo0el9PXqLpR9vusSXBcBBxhEhBiFHjyv0OyfQRa7H3kdJhJcx4+ybm/buSUePK2f3wXXpch4igzqbgftnuSJjmG6Eb4q5BV58C7gq81nvrxO0CMai/FQqPpuUGa5MAFBwMBUcCDhSfCY3/hOC2kHof3CqvWOJVvF+jhPfVGdbj12n6x3VPTueB6/9Fw7ooJ1x8DJHCiN8hmT4ivdkXU0RuBL6O91e9CDhdtX1H7foqKiq0srJyo6+bDzS9DK2/AwgiJecggXI0vRptuNPrdik+CxofgugjoCvWryCwHaS7s2BX0/j2Tv6fpRQiX4HYv1tXjgz/m7e+ewfclZO9FjoAIQjt5iX78J7I0FsRKehGXMaYnhKR+apa0VW53rbYZwHTVTUlIjcA04HLe1ln3lF3LbirvZuLEkA1ha7+FrjVgIMmXkFGPo2uOSWzZ6nrTflHaNsF00qHSb0EqG/3nNKS3DsKrq5dUs+cE9hAP3n4AIg3TWRKQ+mVOOFJnZc3xvSr3u55+qyqNt2VmweM6X1I+UXj/0OrDkSrp6KrT0Q1AW4tuGtpHoqY/hR33c2Q/oyWUS5JOk3qnaoHito9VwChfTop3z7hC+BAycVIYJMNvKjWH8oCSLJ/P32lU2n+ft3D/OyEm3j9mQX9em1jBoJs9rGfAfwji/XlBa27AW88Od76LfH/QeQgCIzJJPJMF0n0r+CMbOm7bhbxhjcOfwgSr0DdDaw3wqW10CRwRoMmILIvBEZ53S01laz/RqF4/fUJ7zpFpyCFxyGhbduWSn4E6S8hvIc3nDG8n7fWjDYCAQj1/P5Bb8y86j4eu/1p4tEErz31Brf+9xdstZutSmlMky4Tu4jMBkZ3cOgqVX0sU+YqvGzz9w3UMw2YBjBu3CAaDieltNy4dMEp9qbpj3gQXfVV0Mw+pOJAybmw7hd4P8owFE5FQttAeG8kuAWEtsF1RkPtFUAnE56S7wDvAXFIPIf3oSxFp90xgS0gtAVEDsUp/Pp6h93oY7Duam97PSmD8ieQ4rNQKYbUO0jBFCTcv4n97bkLiUe9NykR4ZM3P7PEbkwrXSZ2VT1sQ8dF5LvAFOBQ3cCdWFWdAcwA7+ZpD+McsGTIL9Ga70F6BRSe5C2vC4hTBkN+ga69yEvqgfFI4VSIHIDGnoL4K5BYgAa3xQlu0VJh8h06TeqA1/pu/eNtGunSyY88uD3O0Bs6r65hBhBrOT3xMlLwNaT4lA297D71lRP3ZfG7n5OIJQHY+SDr3zemtV51xYjIEXg3Sw9S1Wh2QsovEhyPjHy242MFh8HIWeCuQlMr0NorMyNMlkHiNSAO9dejwYlIZG809ixEH8picMOQsh9tuExgNKQ/9r7XRtQZuaFbsf1i6gVHM3rCKD5f+CX7HlvBZlt29IHSmMGrt33st+FtvzMrs2zsPFU9u9dRDSIS2ARNL4Xai4AYxGZlxoLHvQKqkP4MjdWjay+hub9+g0J4N1/NWhxbAAAM8klEQVQ3oOA4ZMh1iHTxK+BsQktXkoOkPoLw5A2f08dEhP2+sSf7fcPXMIzJWb1K7Kq6VbYCGdSSC2jpMmkEt3XyFogchDbMpOOkXoL3JtA6kbvgDIfMOi8dis2BksWoRiG4Y5vleTU+D43PRcKT29Ur3jIDxpicZjNPc0F4H7yZn0m8/xKX5k5tpxyc4UhkfzT6AG0TdQQZ8QBoPVpzYWYiU2ZGqruWTvvVAahDqzMrQ0oIlRHIkKtAitCaaUAMjRZCyfkgL3ixOcOg8LhsvnJjTB+wxJ4DJDQJht+Dxl8EjUP0Hppb5+4ydO3FOMNuR4vPgIY/09yKDmzmjZoBNDgWku1mqMoo0Cq8kTFBmrt3mmWuoTFvnZea86DotJbnaYTUu8ioud4ywIExiISz/OqNMdlmiT1HSHgXJLwLqorqWm9dlqbWd6ISbfgLuHGQoLd0LwXeUgBNCqdmRsw0JeVC0JWZ7zObbAQnA25mWOWVHYyZdyG4pVc3Ma+O8P6IFELrkTnGmJxmiT3HiAiUXuaNgNE6IAgaR+tuBNQbS150ojeuvfCE5vOcouPRwGaQ+hgN7wGrT2hXswupBVB4Ik7BgWjgTnTtxd7Eo+bunQQ4myNDf4vGZ0NoD6Tw2P554caYrLFle3OQOEOQEY9A5Gi8/vYoXvdLCnQdxGajqUW07kP31qOpgtD2OKHt6Xh2qguNj6CppUhoR5yRz4Azom2RhjuQgoNxhlyHFB6F1l6CW7UPbs0FqLbvyjHG5CJL7DlKgmO9PUjXS9ApcJdC9K/omtO9rhu3Dq2egtb+BF1zFm79n6Gws7GA6nXnkFnvXUrbHs5svAF4a6vHZnkLmMWf97qDjDE5zxJ7DvOWzW1aAjcI0m4iTnK+tz1ecn5mM44o0AiN9yFlvwSn/TT7AJT+CAmMRtX1Rr+kP285HNweSn/S8jhdRcsN14Q3e9YYk/MssecwKTgUhvzaG2JYdgMy/E7arvkS9BJ6YGxm6zyAEAS28sall5wBFOLNISuE8CFQdxPuqsPR+AuQfJ3mm61OOU75YzhOy20XKToRpCTzr7jLZQTSqTTLP1tJItbTVSmNMdlkN09znFN4BBQe0fxYh9wAtVcBDoR2gvBeiIRg6E1o/V0QHIeUXeOdW3Sit2hY4lWIPQ+JWV4l6S+gYSZoqx2TZP1NqCU4HkY+B6mPIbgl4gzvNM51q+s4d6/prFlRQ6Qowu9evo7Nt9o0Kz8DY0zPWGIfYJzCb6CRQ8CtgcDY5hmjUnA4UnB4m7Lq1kHdLzLdLe23xVMouQAabgMZggy9tcPriTMUOtlJqbVn7p5D9dLVJBMpEo1J7rvuES69+5yNfJXr+/D1T7j7x/dTMrSYs2/5P8o36/xNxpjBzhL7ACROGThlXReMPQnplaw/AzWElF7mLbdbclZWYgoXhpGA100UCDoUlGRve7yG2gYuPexaGutiOAGHLz5axl1v/iZr9RuTb6yPPZ9JCR2uwz78nqyvoX7kmYew4/7b4wQcxu8wlu/8pP04+o1X/eUa1PXenNy0y9IPl2WtbmPykbXY81nBkRB/HmLPAkFvZ6Uh03H6YHXGcEGYG565Ouv1Amy+9aaMHFtO1efViMD+x+/dJ9cxJl9YYs9jIgFk6M1+h9FrwVCQ2179FXP/OY+i0gL2n7qX3yEZk9MssZsBoai0kCNOP9jvMIwZEKyP3Rhj8owldmOMyTOW2I0xJs/0KrGLyM9F5G0RWSAiz4rIZtkKzBhjzMbpbYv9RlXdWVV3BZ4ArslCTMYYY3qhV4ldVde1eljMhjfZNMYY0w96PdxRRK4DvgPUAn06Hu31p99k8btfsOfRkxm//Zi+vJQxxgxYorrhRraIzAZGd3DoKlV9rFW56UCBqv6kg7KIyDRgGsC4ceN2X7JkSY8C/c+fZnPHj/5CKpEiGA5yx/xfM2Yb69I3xgweIjJfVSu6Ktdli11VD+vmNe8DngQ6TOyqOgOYAVBRUdHjLpvZ984l1uBt+hAIBnjzuXcssRtjTAd6Oypm61YPjwE+6F04ndtxv+2IFIabLsyWu07oq0sZY8yA1ts+9utFZFu8Bb6XAGf3PqSOfffaEwlFgnxU+Slf/b+DmbTPtn11KTNI1FTV8sitTxAMBZh64RRKh62/2YgxA1GXfex9oaKiQisrK/v9usY0cV2X7259Hqu+WI2IMG77zblrga3xbnJbd/vYbeapGZTWra5j9bI1pFNpUskUn73zOclE0u+wjMkKS+xmUCobUcqIzYYTCAYIhoJM2HEsoXDI77CMyQpbttfklaUfL+fN595h68kT2W7PrTst5zgOv3vllzx8i9fHfvyPpvRjlMb0LUvsJm8sWbiUc/e8wttGT+DK+y5k32M634h72KghnPWrU/oxQmP6h3XFmLwx79/zSSZSxBsTxKMJnpk5x++QjPGFJXaTNybsOJZgyPsQGikKs/XuE32OyBh/WFeMyRt7HTWZs2/+Ls///b/ssN+2fHv6VL9DMsYXNo7dGGMGCBvHbowxg5R1xRiTJ9LpNG/NeQ9xhF2+sgOOY+22wSovE3v1l6t57+WP2HKX8bYCpBk0fnbCTbw5+x0U2GfK7lx534V+h2R8kndv6Us/Xs6Zk37ETWfdwdmTL+OtF97zOyRj+lz92gZeffINGutjxOpjvPjQKyRiCb/DMj7Ju8T+0sPziEfjNNY1Eo/GefyOZ/wOyZg+V1AcIVIQbn5cVFZIKGJLJAxWeZfYN91iE0IF3i90uDDM2O2sK8bkv2AoyK+e+TFb7TaRbXbfghuevRoR8Tss45O862M/8IR9WLJwKS8+9Ao77rcdJ195vN8hGdMvJu29DXfM/7XfYZgcYOPYjTFmgLBx7MYYM0hZYjfGmDyTlcQuIpeIiIpIeTbqM8YYs/F6ndhFZCxwOPB578MxxhjTW9losd8CXAb0/11YY4wx6+lVYheRY4AvVfWtbpSdJiKVIlK5atWq3lzWGGPMBnQ5jl1EZgOjOzh0FXAl8NXuXEhVZwAzwBvu2IMYjTHG9MBGj2MXkZ2A54Bo5qkxwDJgT1Vd0cW5q4Al3bxUOVC9UUH2j1yPD3I/Rouv93I9xlyPD3I/xnKgWFVHdlUwaxOURGQxUKGqWf3BiEhldwbk+yXX44Pcj9Hi671cjzHX44Pcj7En8dk4dmOMyTNZWytGVSdkqy5jjDEbbyC02Gf4HUAXcj0+yP0YLb7ey/UYcz0+yP0Yux2fL4uAGWOM6TsDocVujDGmBwZUYs/VNWlE5Oci8raILBCRZ0Ukp3b3EJEbReSDTIyPishQv2NqT0ROEJH3RMQVkZwZmSAiR4jIhyLyiYhc4Xc87YnITBGpEpF3/Y6lIyIyVkTmiMjCzP/vBX7H1JqIFIjIayLyVia+a/2OqSMiEhCRN0Xkie6UHzCJPcfXpLlRVXdW1V2BJ4Br/A6onVnAjqq6M/ARMN3neDryLjAVmOt3IE1EJADcDhwJTAK+LSKT/I1qPX8BjvA7iA1IARer6vbA3sA5OfYzjAOHqOouwK7AESKyt88xdeQCYGF3Cw+YxE4Or0mjqutaPSwmx2JU1WdVNZV5OA9vMllOUdWFqvqh33G0syfwiap+qqoJ4AHgWJ9jakNV5wJr/I6jM6q6XFXfyHxfh5ecNvc3qhbqqc88DGX+5dTfr4iMAY4G/tTdcwZEYu/JmjR+EZHrROQL4BRyr8Xe2hnAU34HMUBsDnzR6vFScigpDTQiMgHYDXjV30jaynRzLACqgFmqmlPxAbfiNWrd7p6QM3ueZmtNmr6yofhU9TFVvQq4SkSmA+cCP8ml+DJlrsL7aPz3/oytSXdizDEd7QadU625gUJESoCHgQvbfcL1naqmgV0z954eFZEdVTUn7lmIyBSgSlXni8hXunteziR2VT2so+cza9JMBN7K7Lo+BnhDRLpck6Y/4uvAfcCT9HNi7yo+EfkuMAU4VH0a49qDn2GuWAqMbfW4aT0k0wMiEsJL6n9X1Uf8jqczqrpWRF7Au2eRE4kd2A84RkSOAgqAMhG5V1VP3dBJOd8Vo6rvqOooVZ2Qmd26FJjcn0m9KyKydauHxwAf+BVLR0TkCOBy4BhVjXZV3jR7HdhaRCaKSBg4CXjc55gGFPFaY38GFqrqzX7H056IjGwaJSYihcBh5NDfr6pOV9Uxmdx3EvB8V0kdBkBiHyCuF5F3ReRtvC6jnBrSBdwGlAKzMkMy7/Q7oPZE5DgRWQrsAzwpIs/4HVPmhvO5wDN4N/0eVNX3/I2qLRG5H3gF2FZElorImX7H1M5+wGnAIZnfvQWZ1meu2BSYk/nbfR2vj71bQwpzmc08NcaYPGMtdmOMyTOW2I0xJs9YYjfGmDxjid0YY/KMJXZjjMkzltiNMSbPWGI3xpg8Y4ndGGPyzP8Df8X+ynuiX+gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x179bc49d4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data[:,0], data[:,1],s=10,c=pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. Local Outlier Factor\n",
    "* Based on nearest neighbours\n",
    "* Suited for moderately high dimension datasets\n",
    "* LOF computes a score reflecting degree of abnormility of a data.\n",
    "* LOF Calculation\n",
    "  - Local density is calculated from k-nearest neighbors.\n",
    "  - LOF of each data is equal to the ratio of the average local density of his k-nearest neighbors, and its own local density.\n",
    "  - An abnormal data is expected to have smaller local density.\n",
    "* LOF tells you not only how outlier the data is but how outlier is it with respect to all data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import LocalOutlierFactor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {},
   "outputs": [],
   "source": [
    "lof = LocalOutlierFactor(n_neighbors=25,contamination=.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred = lof.fit_predict(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = np.abs(lof.negative_outlier_factor_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x179bc7c7a20>"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x179bc784978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data[:,0], data[:,1],s=s*10,c=pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 7. Outlier Detection using DBSCAN\n",
    "* DBSCAN is a clustering method based on density\n",
    "* Groups data which are closer to each other.\n",
    "* Doesn't use distance vector calculation method\n",
    "* Data not close enough to any cluster is not assigned any cluster & these can be anomalies\n",
    "* eps controls the degree of considering a data part of cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import DBSCAN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [],
   "source": [
    "dbscan = DBSCAN(eps=.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DBSCAN(algorithm='auto', eps=0.3, leaf_size=30, metric='euclidean',\n",
       "    metric_params=None, min_samples=5, n_jobs=None, p=None)"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dbscan.fit(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x179bc708978>"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x179ba7c7ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(data[:,0], data[:,1],s=s*10,c=dbscan.labels_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
